Method, medium, and system for social media-based recommendations

ABSTRACT

A user may request a recommendation for an item from other users. Other users may respond to the request by recommending for or against items. Users may up-vote or down-vote the recommendations or responses of other users. The recommendations of the other users may be used to identify items and provide one or more recommendations to the requesting user. The original question and the responses may form a conversation thread. The recommendations may be inserted into the thread as responses, may be presented alongside the thread, or may be presented at the end of the thread. The recommendations may be based on one or more attributes of the user. The weight of the recommendations provided by other users may vary.

CLAIM OF PRIORITY

The application is a Continuation of U.S. application Ser. No.17/386,732, filed Jul. 28, 2021, which is a Continuation of U.S.application Ser. No. 14/457,963, filed Aug. 12, 2014, now issued as U.S.Pat. No. 11,120,491, which claims priority to U.S. ProvisionalApplication No. 61/881,810, filed Sep. 24, 2013, each of which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the processingof data. Specifically, the present disclosure addresses systems andmethods for social media-based recommendations.

BACKGROUND

Users can browse items on merchant and auction sites to find desireditems. The sites may provide a search function or divide items intocategories, to help users find items more quickly. Merchant and auctionsites can also provide recommendations of items to users based on pastbehavior of the users.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a network diagram illustrating a network environment suitablefor social media-based recommendations, according to some exampleembodiments.

FIG. 2 is a block diagram illustrating components of an applicationserver suitable for social media-based recommendations, according tosome example embodiments.

FIG. 3 is a block diagram illustrating components of a client machinesuitable for social media-based recommendations, according to someexample embodiments.

FIG. 4 is a block diagram illustrating a user interface suitable forsocial media-based recommendations, according to some exampleembodiments.

FIGS. 5-7 are screen diagrams illustrating an example conversationthread suitable for social media-based recommendations, according tosome example embodiments.

FIG. 8 is a flowchart illustrating operations of an application serverin performing a method of social media-based recommendations, accordingto some example embodiments.

FIG. 9 is a flowchart illustrating operations of an application serverin performing a method of social media-based recommendations, accordingto some example embodiments.

FIG. 10 is a flowchart illustrating operations of an application serverin performing a method of social media-based recommendations, accordingto some example embodiments.

FIG. 11 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

Example methods and systems are directed to social media-basedrecommendations. Examples merely typify possible variations. Unlessexplicitly stated otherwise, components and functions are optional andmay be combined or subdivided, and operations may vary in sequence or becombined or subdivided. In the following description, for purposes ofexplanation, numerous specific details are set forth to provide athorough understanding of example embodiments. It will be evident to oneskilled in the art, however, that the present subject matter may bepracticed without these specific details.

A computer user may have an account on a social network (e.g., Facebook,Twitter, LinkedIn, etc.). The user can have relationships with otherusers in the social network (e.g., family, friends, and so on). The usercan also have relationships with other entities in the social network(e.g., the user may be a member of a group, an employee of a company,etc.). The social network may be a social portion of a commerce site.

The user can request a recommendation for an item from other users.Other users can respond to the request by recommending for or againstitems. For example, one user may recommend a certain brand or productwhile another user may recommend against the brand and recommend adifferent brand or product. In some example embodiments, users areenabled to up-vote or down-vote the recommendations or responses ofother users.

Items can be informational items (e.g., news articles, blogs, images,multimedia content, etc.), transactional items (e.g., items for saleonline, items for sale in brick-and-mortar locations, items wantedonline, items wanted in brick-and-mortar locations), or other types ofitems. The item can be an item for purchase (e.g., a car, a pair ofshoes, a movie ticket, etc.), an article (e.g., a news article, a buyingguide, etc.), a web site (e.g., a news site, a shopping site, etc.), aperson (e.g., a social contact, a professional contact, a domain expert,etc.), or any other item that other users may be able to recommend. Anitem may be a collection of items. For example, each toy in a line oftoys (e.g., a single Star Wars figure) can be treated as an item and thecollection of toys in the line of toys (e.g., all Star wars figures) canalso be treated as an item. As another example, a social mediaapplication or online marketplace application may facilitate thecreation of collections of items by users and the created collectionsmay be items available to be recommended.

The recommendations of the other users are used to identify items andautomatically provide one or more recommendations to the user requestingthe recommendation. The provided recommendations are presented in a userinterface and operable to direct the user to the item. For example, therecommendation may be for a web site and presented as a hyperlink to theweb site. As another example, the recommendation may be for a productavailable for purchase on another site and presented as a user interface(“UI”) element that, when clicked on, presents the user with theopportunity to purchase the item without leaving the original site.

The original question and the responses form a conversation thread. Therecommendations can be inserted into the thread as responses, presentedalongside the thread, or presented at the end of the thread. Therecommendations may be presented automatically or hidden unlessrequested by the user. The recommendations may be presented to allparticipants in the thread or only to the user requesting therecommendation.

The recommendation can be based on one or more attributes of the user.For example, a user may have identified a set of interests (e.g., amovie, a sports team, a band, a celebrity, a city, or any other item orcategory of items) and the recommendation may be based on the user'sinterests. Additionally, other information about the user may be known.To illustrate, a user may be located in New York City and have requestedrecommendations for shoes. Based on recommendations by other users andthe user's location in New York City, a particular shoe at a particularshoe store in New York City can be recommended. As another illustration,a user may have indicated an interest in an actor and requestedrecommendations for movies. Other users may recommend several movies,one of which contains the actor. Accordingly, the user-recommended moviewith the actor of interest can be recommended by the recommendationsystem.

The weight of the recommendations provided by other users may vary. Forexample, a user may set a weight for other users. To illustrate, a usermay choose to give family members twice the weight of friends, or togive a user that typically gives bad advice a weight of zero, or even anegative weight. In some example embodiments, the weights for users areautomatically generated, and may be based on the previous interactionsof the recommending user with items and the previous interactions ofother users with items recommended by the recommending user.

Interactions with items can include viewing items, bidding on items,buying items, subscribing to items, sharing the items on socialnetworks. In some example embodiments, only a subset of the interactionsis considered. For example, only buying an item may be considered to bean interaction with the item. Additionally, different types ofinteractions can be considered in a single embodiment. To illustrate, anexample embodiment may consider any form of interaction by the currentuser to be an interaction but consider only purchases by other users tobe interactions. Thus, while the description below frequently refers tointeractions, the various possible combinations of types of interactionsshould be recognized as being within the scope of the present inventivesubject matter.

In some example embodiments, interactions are grouped into categories ofrelevance and the categories utilized as a basis for the selection ofresults. An example of a relevance category may be interactions thatindicate the end of a shopping session. Interactions that may beincluded in such a relevance category could include, for example,purchasing an item, performing a new search in a different itemcategory, ending the browsing sessions, navigating to a completelydifferent website, etc.

As mentioned above, the weight attributed to a recommendation by a usercan be based on the interactions of the user with items. For example, auser that interacts with many clothing items may be given a higherweight when recommending clothing items. Similarly, a user whoserecommendations are often followed (e.g., whose recommendations oftenresult in interactions with the recommended items) may be given agreater weight. The weight can be based on the types of interactionsthat occur. For example, a user whose recommendations frequently resultin item purchases may have a greater weight than a user whoserecommendations frequently result in item views without leading topurchases. User weights can be broken down by category. For example, auser whose food recommendations are often followed but whose clothingrecommendations are rarely followed may have a high weight given to hisfood recommendations but a low weight given to his clothingrecommendations. Example interactions that may improve the weight givento a user for a recommendation include items bought and sold by the user(e.g., a user who has bought and sold many guitars may have greatercredibility when recommending guitars), items commented on by the user(e.g., a user who shares opinions frequently regarding a category ofproducts may have greater credibility within that category), comments bythe user that are shared by other users (e.g., a user whose comments ona topic are frequently shared by other users may have greatercredibility on the topic), and the field of expertise of the user (e.g.,a user with a Music degree may have greater credibility with respect toinstrument recommendations).

FIG. 1 is a network diagram depicting a network environment 100, withinwhich one example embodiment can be deployed. A networked system 102, inthe example forms of a network-based marketplace or publication system,provides server-side functionality, via a network 104 (e.g., theInternet or Wide Area Network (WAN)) to one or more clients. FIG. 1illustrates, for example, a web client 106 (e.g., a browser), and aprogrammatic client 108 executing on respective client machines 110 and112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more marketplace applications 120, social applications 121,and payment applications 122. The application servers 118 are, in turn,shown to be coupled to one or more database servers 124 that facilitateaccess to one or more databases 126.

The marketplace applications 120 provide a number of marketplacefunctions and services to users that access the networked system 102.The social applications 121 provide a number of social functions andservices to users that access the networked system 102. The socialapplications 121 may use a relationship graph to track relationshipsbetween users (e.g., friends). The social applications 121 may allowusers to share messages, photos, videos, and other forms ofcommunication and data. The payment applications 122 provide a number ofpayment services and functions to users. The payment applications 122may allow users to accumulate value (e.g., in a commercial currency,such as the U.S. dollar, or a proprietary currency, such as “points”) inaccounts, and then later to redeem the accumulated value for products(e.g., goods or services) that are made available via the marketplaceapplications 120. While the marketplace, social, and paymentapplications 120, 121, and 122 are shown in FIG. 1 to each form part ofthe networked system 102, it will be appreciated that, in alternativeembodiments, the social applications 121 may form part of a socialnetwork that is separate and distinct from the networked system 102.Likewise, the payment applications 122 may form part of a paymentservice that is separate and distinct from the networked system 102.

Further, while the network environment 100 shown in FIG. 1 employs aclient-server architecture, the embodiments of the present invention areof course not limited to such an architecture, and could equally wellfind application in a distributed, or peer-to-peer, architecture system,for example. The various marketplace, social, and payment applications120, 121, and 122 could also be implemented as standalone softwareprograms, which do not necessarily have networking capabilities.

The web client 106 accesses the various marketplace, social, and paymentapplications 120, 121, and 122 via the web interface supported by theweb server 116. Similarly, the programmatic client 108 accesses thevarious services and functions provided by the marketplace, social, andpayment applications 120, 121, and 122 via the programmatic interfaceprovided by the API server 114. The programmatic client 108 may, forexample, be a seller application (e.g., the TurboLister applicationdeveloped by eBay Inc., of San Jose, Calif.) to enable sellers to authorand manage listings on the networked system 102 in an off-line manner,and to perform batch-mode communications between the programmatic client108 and the networked system 102. As another example, the programmaticclient 108 may be a social networking application to enable users tocommunicate with members of a social network (e.g., via the socialapplication 121).

The client machine 110 or 112 can present information to a user. Forexample, the client machine 110 may be running a web browser presentinga web page. The user may indicate a request for a recommendation to theclient machine 110. A request for a recommendation defines theparameters of the request. A request for a recommendation can include analphanumeric string, an image, audiovisual data, or any suitablecombination thereof. A request for a recommendation can include filtersthat exclude items complying with or not complying with the filter. Arequest for a recommendation may be composed of multiple elements. Anelement is a discrete portion of a request for a recommendation, such asa word or phrase in an alphanumeric string, an image, or a filter. Forexample, the user may type a request for a recommendation into a textfield (e.g., a comment or message box), select an item to requestrecommendations for similar or related items, upload an image to requesta recommendation for similar or related items, or any suitablecombination thereof. One item is similar to another if they aresubstitutes for each other. For example, one television may be similarto another television. An item is related to another if they worktogether or are frequently purchased together. For example, peanutbutter may be related to jelly, or a universal remote control may berelated to a television.

The client machine 110 or 112 can submit a request for a recommendationto an application server 118 running a social application 121. Theapplication server 118 can publish the request for a recommendation toother client machines 110 or 112. The other client machines 110 or 112can submit responses to the request for recommendation. The responses tothe request for recommendation may be published by the socialapplication 121 to the requesting client machine 110 or 112. The socialapplication 121 may generate an item recommendation for the requestingclient machine 110 or 112. The recommendation may be for results withina certain category (e.g., a product category such as books, games,movies, furniture, etc. or a content category such as news, blogs,fiction, opinion, entertainment, and the like), with a certain attribute(e.g., produced within a certain date range, located within a geographicarea, shipped in a certain way, sold in a particular way, etc.), orrelated to a certain entity (e.g., liked by a friend, of interest to agroup, sold by a company, etc.). The generated item recommendation canbe published to the requesting client machine 110 or 112.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more promotional,marketplace, social, or payment functions that are supported by therelevant applications of the networked system 102.

FIG. 2 is a block diagram illustrating components of an applicationserver 118 running a social application 121, according to some exampleembodiments. The application server 118 is shown as including acommunication module 210, a recognition module 220, a generation module230, and a storage module 240, all configured to communicate with eachother (e.g., via a bus, shared memory, a switch, or applicationprogramming interfaces (APIs)). Any one or more of the modules describedherein may be implemented using hardware (e.g., a processor of amachine) or a combination of hardware and software. For example, anymodule described herein may configure a processor to perform theoperations described herein for that module. Moreover, any two or moreof these modules may be combined into a single module, and the functionsdescribed herein for a single module may be subdivided among multiplemodules. Furthermore, according to various example embodiments, modulesdescribed herein as being implemented within a single machine, database,or device may be distributed across multiple machines, databases, ordevices.

The communication module 210 controls communication with the clientmachine 110 and the database 126. The communication module 210 may alsosend data for storage on the application server 118 or the database 126.

The communication module 210 can receive a request for a recommendationfrom the client machine 110 or 112. Upon receiving a request for arecommendation, the recognition module 220 can parse the request forrecommendation to identify a category of the request. To illustrate, therequest may be “Can anyone recommend a good car?” For this request, thecategory may be “automobiles.” As another illustration, the request maybe, “My Explorer is just about dead. Can anyone recommend a goodreplacement?” Recognizing that the request is for a replacement for anExplorer, and that an Explorer is an automobile, the category may againbe “automobiles.” In some example embodiments, the category of therequest is determined by identifying the category of items with the mostitems responsive to the request or which has received the mostinteractions based on similar requests.

The communication module 210 can receive responses to the request for arecommendation from other client machines 110 or 112. The communicationmodule 210 may send the responses to the request to the requesting user,and provide the ability for the users to communicate via the socialapplication 121. The recognition module 220 can also parse the responsesto identify recommended items. To illustrate, the response may be “I'mhappy with my Corolla.” For this response, the item may be “ToyotaCorolla.” As another illustration, the response may be “The Tesla ModelS got great reviews.” For this response, the item may be “Tesla ModelS.” Additionally, the responses can be parsed to determine if therecommendations are positive or negative. For example, “I'm happy withmy Corolla” is a positive recommendation, while “The transmission on myCorolla failed as soon as my warranty expired” is a negativerecommendation.

The generation module 230 can generate a recommendation for an itembased on the request for a recommendation and the responses to therequest received from the client machines 110 or 112. For example, ifthe request for a recommendation indicates that the requestedrecommendation is for a car and several responses recommend a ToyotaCorolla, the generation module 230 may generate a recommendation for aToyota Corolla.

The generation module 230 can generate a recommendation for an itembased on recommendations received from users without separatelyrecognizing a request for a recommendation. For example, a news site maypost an article about a product preview and invite user comments. Theuser comments may reflect opinions about items related to the previewedproducts. Based on the comments, the generation module 230 can generatean item recommendation.

The generation module 230 can also retrieve a location associated withthe user from the storage module 240, or a location of the user may becommunicated from the client machine 110 or 112 to the generation module230 via the communication module 210. For example, the user can set apreferred location (e.g., a home address, a work address, or a vacationdestination) as a preference, and the preferred location may be used asthe user location by the generation module 230. As another example, theclient machine 110 or 112 may have a built-in GPS sensor, and thelocation of the client machine 110 or 112 may be transmitted to theapplication server 118 along with the query.

The generated recommendation can be a recommendation for items local tothe requesting user. For example, if the request is for an item topurchase, the generated recommendation may be for an item available topurchase from a retail location near to the requesting user. As anotherexample, if the request is for sights to see, the generatedrecommendation may be for an attraction near to the requesting user.

Items presented to the querying user may be items for sale. For example,a user may be provided a recommendation for a remote-control caravailable for purchase at a local hobby store. Additionally oralternatively, information about online purchases of the item may alsobe presented. For example, an auction in an online marketplace of thesame or a similar item may be presented. Similarly, a user may beprovided information about an upcoming nearby concert and how to buytickets locally may be presented to the user. Additionally oralternatively, information about online sales of the concert tickets maybe presented.

The communication module 210 can send the recommendation provided by thegeneration module 230 to the client machine 110 or 112, for display tothe user. For example, data may be transmitted to the web client 106running on the client machine 110 using JavaScript Object Notation(“JSON”). JavaScript running in the web client 106 can parse the JSONobject and add the recommendation to a web page, for viewing by theuser.

Although this document generally refers to the “display” of information,the presentation of results to the user may be an audio presentation.For example, the names and addresses of important monuments, localstores, or upcoming events may be read aloud to the user. As anotherexample, a user may be exploring a zoo, amusement park, museum, fair, orthe like and receive audio instructions providing directions to arecommended exhibit or attraction.

FIG. 3 is a block diagram illustrating components of a client machinesuitable for social media-based recommendations, according to someexample embodiments. The client machine 110 or 112 is shown as includinga communication module 310, a generation module 320, and a userinterface module 330, configured to communicate with each other (e.g.,via a bus, shared memory, or a switch). Any one or more of the modulesdescribed herein may be implemented using hardware (e.g., a processor ofa machine) or a combination of hardware and software. For example, anymodule described herein may configure a processor to perform theoperations described herein for that module. Moreover, any two or moreof these modules may be combined into a single module, and the functionsdescribed herein for a single module may be subdivided among multiplemodules. Furthermore, according to various example embodiments, modulesdescribed herein as being implemented within a single machine, database,or device may be distributed across multiple machines, databases, ordevices.

The communication module 310 can communicate with the application server118, the network 104, or any suitable combination thereof. Informationreceived via the communication module 310 may be presented (e.g.,displayed on a display device or played on an audio device) via the userinterface module 330. Information may be selected or requests forrecommendations can be entered by a user using a user interfacepresented by the user interface module 330. The requests forrecommendations may be communicated to the application server 118 viathe communication module 310. The application server 118 can respond tothe requests for recommendations with one or more recommendations,received by the communication module 310. Recommendations generated bythe application server 118 can be received by the communication module310 and presented to the user by the user interface module 330. Forexample, the recommendations may be presented in the form of a banneradvertisement, a text advertisement, a message in a message thread, apop-up window, or another user interface element.

The generation module 320 identifies the user's recommendation requestand the responses, and generate an appropriate query. For example, therecommendation request and responses may be parsed on the client machine110 or 112 to identify the category of the request and the recommendeditems. Based on the category, the recommended items, user data, and thelike, a query can be generated. To illustrate, a thread created by auser that lives in Los Angeles who has requested a recommendation for acar and received several recommendations for a Toyota Corolla may causethe generation of a query for “Toyota Corolla in Los Angeles.” Thegenerated query can be transmitted to the application server 118 by thecommunication module 310 for processing. One or more results for thesearch query can be presented to the user as recommendations. In someexample embodiments, the generation module 320 is not integrated intothe social media tool. For example, the generation module 320 may bepart of a web browser plug-in, capable of providing recommendations whenthe user interacts with social media websites without requiring thecooperation of those social media websites. In other exampleembodiments, the generation module 320 is integrated with the socialmedia tool. For example, a social media app may use inter-processcommunications to inform a recommendation app of the recommendationrequest and responses.

The user interface module 330 may present updated recommendations astime passes or additional responses are received. For example, a newrecommendation may be presented at set periods (e.g., every 5 minutes,every hour, and so on).

As another example, a new recommendation may be presented after eachbatch of comments is received. A batch of comments is a set of one ormore comments that occur within a set period of time from each other.For example, if the timeout period is five minutes, then as long asfewer than five minutes passes between responses, the batch willcontinue to grow. Once the timeout period passes without a comment, thebatch is complete. To illustrate, seven comments may be received withperiods between them of thirty seconds to three minutes. Five minutesafter the seventh comment is received, a recommendation may bepresented. An hour later, another comment may be received, with nosubsequent comments. Five minutes after this comment, anotherrecommendation may be presented. In some example embodiments, thetimeout period is calculated dynamically, based on the momentum of thethread. For example, the time period between the presentation of therequest and the first response may be greater than the time periodbetween the first response and the second response, indicating that thethread is gaining momentum. An increase in the time period may bedetected as a decrease in momentum, and a recommendation presented whenthe decrease in momentum is detected. Machine learning algorithms mayestablish a method for calculating momentum and determining the time toinsert the recommendation.

Other factors that can affect the timing of the presentation ofrecommendations by the user interface module 330 include choosing topresent recommendations after a certain number of responses have beenreceived, after a certain number of items have been identified, after acertain strength of recommendation has been reached (e.g., several usershave agreed on a recommended product), or after an authority has weighedin. For example, a recommendation may be provided as soon as threeresponses are received, regardless of the content of those responses. Asanother example, a recommendation may be provided after three differentitems have been identified. As another example, a recommendation may beprovided when a recommendation of strength 2 is calculated (e.g., arecommendation by a single user of weight 2, a recommendation by twousers of weight 1, or a twice-liked recommendation from a user of weight1, where each like has a weight of 0.5). In some example embodiments, arecommendation is provided when an item is recommended by a certainnumber of users, regardless of weight. In other example embodiments, arecommendation is provided as soon as a user with a higher weightrecommends an item, regardless of the number of items identified.

The user interface module 330 can present recommendations at anidentified time of day. For example, the user receiving therecommendation or the server providing the recommendation can set aparticular time or times for presenting or updating recommendations(e.g., at 7:30 AM, 12:05 PM, and 6:25 PM). The identified time of daymay be based on an analysis of prior user interactions. For example, ifa recommendation provided during the workday generates a higherinteraction rate than recommendations provided at night, recommendationsmay be provided as soon as they are generated for requests posted duringthe workday, but delayed until the next morning for requests posted atnight. The identified time of day based on prior user interactions maybe based on the user asking for the recommendation, other users thathave asked for recommendations in the same category, all users, and thelike. For example, if the user asking for the recommendation hasinteracted with many items between the hours of 6 and 9 PM, but neverinteracted with an item between the hours of 1 and 6 PM, anyrecommendation generated between 1 and 6 PM may be held until 6 PM forthat user.

FIG. 4 is a block diagram 400 illustrating a user interface suitable forsocial media-based recommendations, according to some exampleembodiments. As can be seen in block diagram 400, the user interface istitled “My Page” and includes a request for a recommendation 410, threeresponses to the request 420 a-420 c, and a recommendation 430 for anitem. The recommendation 430 can be presented as a hyperlink, operableto retrieve more information about the recommended item. Additionalinformation can also be presented about the recommended item, such aslocation, price, color, style, size, modification date, and so on. Theinformation presented may depend on the item itself. For example, a newsarticle item may be presented along with its publication date, an onlineauction item may be presented with its current price and auction closingdate, or an item at a retail store location may be presented with thestore address.

The recommendation 430 can include information regarding one or moreitems. One or more nearby products may be displayed in response to thequery, indicating items for sale that are near to the user. For example,a store near the user may have a recommended item for sale. Therecommendation 430 can also indicate items that are wanted for purchase.For example, a coin shop or a pawn shop may be interested in purchasingitems from the user. One or more nearby events may be recommended,indicating events that are near to the user, such as concerts,festivals, movies, sporting events, and the like. One or more nearbylocations may be recommended, indicating locations that are near to theuser, such as monuments, stores, gas stations, restaurants, stadiums,and other locations of interest. One or more nearby people may berecommended, indicating people that are near to the user, such asfriends of the user, celebrities, and other people of interest. In someexample embodiments, one or more of the items, events, locations, andpeople displayed is chosen based on an advertising fee paid.

Interactions with the recommendation 430 may be tracked and added to atracking database. For example, the recommendation 430 may be based onthe recommendations provided by Friend2 and Friend3. A recordcorresponding to the recommendation 430 may be created, and linked tothe users corresponding to Friend2 and Friend3. After receiving therecommendation 430, the user “Me” may click on, touch, or otherwiseinteract with the recommendation 430. When the recommendation 430 isinteracted with, the record for the recommendation 430 can be updated.For example, if the text “Nike Air Jordan on eBay” is a hyperlink, thehyperlink may include an argument whose value is a unique identifier forthe recommendation 430. Accordingly, when the user accesses thehyperlink, the server can identify and update the record correspondingto the recommendation 430. Additionally or alternatively, the hyperlinkmay direct the user to an intermediate server that tracks the use of thehyperlink and then redirects the user to the destination.

FIGS. 5-7 show a conversation thread 500. The conversation thread 500includes a recommendation request 510, social media options 520, andcomments 530 a-530 v.

An item to be recommended may be determined by aggregating therecommendations provided by the other users. The initial comment maycontain the request for recommendation, as in the conversation thread500, or a later comment in the thread may contain the request. In thethread 500, the comment “I am finally thinking about replacing theExplorer. Can't be a minivan, but a third row would really be handy.Recommendations?” may be detected as a request for a recommendation fora car, given identification of Explorer as a car and the explicitrequest for recommendations. In other cases, the request forrecommendations may be inferred from the content of the comment, thecategory for which requests are sought may be made explicit, etc.

The comment 530 a is “I'd be happy to sell you my Q7 for more than Ipaid . . . . Just kidding. I do like it though.” The comment 530 a maybe parsed to identify the “Q7” as being “like[d],” and the Q7 correlatedusing a database to be identified as a car.

The comment 530 b, from another user, states “Lexus GX470 (stoppedmaking in 2010 . . . they have the new GX460 but I think it looks like atransformer).” The comment 530 b may be parsed to identify arecommendation for the “Lexus GX470” and a recommendation against the“GX460.” The determination that a recommendation is for or against maybe based on identification of words near the recommended product (e.g.,“like” or “great” may indicate a positive recommendation while “hate” or“awful” may indicate a negative recommendation). Alternatively oradditionally, the determination that a recommendation is for or againstmay be based on a human's interpretation of the comment, anatural-language parser's interpretation, etc. The comments 530 a-530 vmay be aggregated, for example as in the table shown below.

Item Comments Net Result Likes Audi Q7 1 1 Lexus GX 470 1 1 Acura MDX 1−1 0 1 Toyota 1 1 2 1 Highlander Mercedes E350 1 1 Mercedes R350 1 1Volvo XC 70 1 1 GMC Acadia 1 1 1 3 Honda Pilot −1 −1 0 1 −1 DodgeDurango 1 1 Tesla 1 1 Mazda 5 1 1 BMW Hybrid 1 1 Conversion Van 1 1Tesla Model X 1 1 Mazda CX-9 1 1

The value of each comment may be based on the weight of the userproviding the recommendation. For example, if the user “Ray,” providingthe comment 530 a, had a weight of two when recommending cars, then thevalue in the first column next to the Audi Q7, recommended by Ray, couldbe 2 instead of 1. In that case, the Net Result value for the Audi Q7would be 2 instead of 1. Also shown in the table are the number of likesreceived by each recommendation. In the example embodiment shown, thenumber of likes is not used to determine the net recommendation value.In other example embodiments, a like may be considered as equal in valueto a comment or equal to a fraction of a comment. For example, one likewas received for a comment recommending the Acura MDX. The like may betreated as half of a comment, bringing the net score for the Acura MDXto 0.5.

A user's weight may be negative. For example, if the user asking for therecommendation has frequently down-voted the recommending user'sprevious recommendations, a positive recommendation by the recommendinguser may be treated as a negative recommendation.

Information about the user requesting the recommendation may also beconsidered in generating the recommendation. For example, age, gender,location, income level, education level, known product affinities (e.g.,preferred brands, items owned, etc.), and the like may be considered. Toillustrate, a request for recommendations for shoes by a woman maygenerate recommendations for women's shoes, while the same responses byusers to a request by a man may generate recommendations for men'sshoes.

In some example embodiments, the level of expertise regarding thecategory for which a recommendation is requested or the level ofvagueness of the recommendation request may be detected. For example,rather than asking “Can anyone recommend a good new sedan?”, whichrequests a specific item, a user may post a request such as “Should Ibuy a new car or a used one?”, which asks for more general information.Based on detecting the broad nature of the query, a recommendation for aguide may be generated rather than a recommendation for a specificproduct. For example, a “How to buy a car” guide may be presented ratherthan shopping links to cars identified by users responding to therequest.

FIG. 8 is a flowchart illustrating operations of an application server118 in performing methods of social media-based recommendations,according to some example embodiments. Operations in the method 800 maybe performed by the application server 118 running a social application121, using modules described above with respect to FIG. 2 . As shown inFIG. 8 , the method 800 includes operations 810, 820, and 830.

In operation 810, the application server 118 may receive communicationsfrom users (e.g., using the communication module 210) and detectrecommendations in the communications (e.g., using the recognitionmodule 220). For example, one user may recommend one brand of shoe,another user may recommend a different brand of shoe, and a third usermay up-vote or “like” the first user's recommendation. The detectedrecommendations may have been sent in response to a query from anotheruser, generated in response to presented content, or spontaneouslygenerated. For example, a web page may provide information regardingpopular computer games. The web page may allow comments by visitors. Thevisitors to the web page may post comments including their opinions onvarious games. In operation 810, those comments are interpreted todetect recommendations (positive or negative) from users regarding thevarious games. As another example, a general-purpose forum may allowusers to create conversation threads regarding different topics. A usermay create a thread to discuss a favorite movie. Other users may respondwith their opinion on the favorite movie, and also by expressingopinions about other movies. In operation 810, those opinions areinterpreted to detect recommendations from users regarding the othermovies.

In operation 820, based on the recommendations from the users,attributes of the requesting users, weights assigned to eachrecommending user, the time of day, and other factors, an item torecommend may be determined (e.g., by the generation module 230). Forexample, a shoe of the brand recommended by the first user and liked bythe third user that is available at a store that is currently open thatis nearest to the user may be determined.

In operation 830, a recommendation for the identified item may bepresented (e.g., communicated by the communication module 210 to theclient machine 110 for presentation by the user interface module 330 tothe user). For example, the recommendation may appear as a response to auser's recommendation request in the conversation thread between theusers. In some example embodiments, the recommendation appears as thoughthe recommender were a participant in the thread. As another example,the recommendation may appear at the end of the thread, with newresponses from users pushing the recommendation down. As a furtherexample, the recommendation may appear at the bottom of the screen or toone side of the screen, with the position being unaffected by theprogress of the thread.

A plurality of recommendations may be presented. The number ofrecommendations may be predetermined (e.g., one recommendation, threerecommendations, etc.) or dynamically determined based on the categoryof the item, the strength of the recommendations, and so on. Forexample, if three different items are recommended, but one item isrecommended at strength 10 while the others are at strength 1, only thestrongest recommendation may be presented. The strength of arecommendation for an item may be the sum of the weights of therecommending users minus the sum of the weights of the usersrecommending against the item. As another example, an analysis of prioruser interactions with recommendations for the category of shoes mayshow that providing three recommendations increases the probability thatthe user will interact with one of the items relative to providing onlyone recommendation. In this example, three recommendations may beprovided. The number of recommendations may take into account multiplefactors. Combining the examples above, the application server 118 maydetermine that one recommendation should be presented based on thestrength of the recommendation and that three recommendations should bepresented based on the category of the recommendation and blend thevalues to determine that two recommendations should be presented.

FIG. 9 is a flowchart illustrating operations of an application server118 in performing methods of social media-based recommendations,according to some example embodiments. Operations in the method 900 maybe performed by the application server 118 running a social application121, using modules described above with respect to FIG. 2 . As shown inFIG. 9 , the method 900 includes operations 910, 920, 930, and 940.

In operation 910, the application server 118 receives a communicationfrom a user (e.g., by the communication module 210) and detects arecommendation request in the communication (e.g., by the recognitionmodule 220). For example, the user may request a recommendation for apair of shoes. The request for a recommendation may be the first commentin the thread, or it may be a later comment in the thread. For example,a user may make a social posting describing a recent flight. Anotheruser may ask about how the first user felt about the airline. The firstuser may respond with a positive or negative recommendation about theairline, while a third user mentions another airline he had a positiveor negative experience with. In this example, the question about how thefirst user felt about the airline may be recognized as a recommendationrequest.

In some example embodiments, a category of the request may changedynamically. For example, if the first detected request regards a car,and other users begin to respond with comments that identify specificcars, a recommendation for a car may be generated and presented to theuser. If a user then mentions that some cars are better than others forinstalling infant car seats, the subject of the conversation may fluidlychange to a discussion of the best car seats available. This change oftopic may be detected as a request for a recommendation for an infantcar seat. By detecting a change of topic, recommendations for differentcategories may be generated within a single thread.

In operation 920, a batch of comments is detected (e.g., by therecognition module 220 or the generation module 230). A batch ofcomments is a set of one or more comments. In some example embodiments,a batch is identified as a set of comments that occur within a setperiod of time from each other. For example, if the timeout period isfive minutes, then as long as fewer than five minutes passes betweenresponses, the batch will continue to grow. Once the timeout periodpasses without a comment, the batch is complete. To illustrate, sevencomments may be received with periods between them of thirty seconds tothree minutes. Five minutes after the seventh comment is received, arecommendation may be presented. An hour later, another comment may bereceived, with no subsequent comments. Five minutes after this comment,another recommendation may be presented.

In some example embodiments, the timeout period for defining a batch ofcomments is calculated dynamically, based on the momentum of the thread.For example, the time period between the presentation of the request andthe first response may be greater than the time period between the firstresponse and the second response, indicating that the thread is gainingmomentum. An increase in the time period may be detected as a decreasein momentum, and a recommendation presented when the decrease inmomentum is detected. Machine learning algorithms may establish a methodfor calculating momentum and determining the time to insert therecommendation.

Additionally or alternatively, a batch of comments may have a minimum ormaximum size. The minimum and maximum sizes may be the same. Forexample, a batch may be identified as a set of five comments, regardlessof the time between the comments. In other example embodiments, aminimum size is combined with a timeout period. For example, if theminimum size is three comments, a recommendation may be presented aftera timeout is detected only if the batch is at least three comments. Ifthe batch is below the minimum size, any additional comments may beadded to the batch without regard to the elapsed time. Alternatively,the batch can be allowed to expire after the timeout period, and any newcomments added to a new batch.

In operation 930, an item to recommend is determined based on the batch(e.g., by the generation module 230). For example, the methods describedabove with respect to the operation 820 may be used to determine theitem to recommend.

In operation 940, a recommendation for the item is presented. Forexample, a comment including information about the item can be added tothe end of a conversation thread that includes the request and the batchof user recommendations. After the recommendation for the item ispresented, the method 900 again checks to see if a batch of userrecommendations has been detected (in operation 920). In this way, themethod 900 can provide multiple item recommendations in response to asingle request for recommendation, one for each detected batch of userrecommendations.

FIG. 10 is a flowchart illustrating operations of an application server118 in performing methods of social media-based recommendations,according to some example embodiments. Operations in the method 1000 maybe performed by the application server 118 running a social application121, using modules described above with respect to FIG. 2 . As shown inFIG. 10 , the method 1000 includes operations 1010-1060.

In operation 1010, the application server 118 accesses communicationsfrom users (e.g., using the communication module 210 or the storagemodule 240) and detects recommendations in the communications (e.g.,using the recognition module 220). This operation may be performedsimilarly to operation 810, described above with respect to FIG. 8 .

In operation 1020, each detected recommendation is considered, andoperations 1030-1050 are performed for that recommendation.

In operation 1030, the user providing the recommendation is identified(e.g., by the generation module 230). For example, the applicationserver 118 may have access to a data record for each comment (e.g.,stored by the storage module 240), including a user identifier and thetext content of the comment. The text content is analyzed in operation1010 to identify the recommendation, and the user identifier is accessedin operation 1030 to identify the user providing the recommendation. Arecord containing the user identifier, the recommendation, the comment,the time of the recommendation, data regarding the user receiving therecommendation, or any suitable combination thereof may be created. Asanother example, a web-harvesting application may download the text of aweb page. The text of the web page may include user names or user photosalong with the text of the comments. The text of the comments may beparsed in operation 1010 to identify recommendations. The user names oruser photos may be identified based on proximity to the comments as anidentifier of the user posting the comment. Accordingly, theweb-harvesting application can also create a database that tracks therecommendation activity of users.

In operation 1040, prior recommendations provided by the user areaccessed (e.g., from the storage module 240). For example, withreference to FIGS. 5-7 , the present recommendation provided by the usermay be for a particular make and model of car, but the same user mayalso be a participant in the conversation of FIG. 4 , in which therecommendation was for a brand of shoe. Accordingly, a databasecontaining records with data regarding previous recommendations by thesame user may be accessed in operation 1040.

In operation 1050, the recommendation provided by the user is given aweight based on the number of prior recommendations generated from theuser that were followed by other users. For example, as discussed abovewith respect to FIG. 4 , when a presented recommendation is followed, adatabase record for the recommendation can be updated. Thus, allrecommendation records linked with the recommending user can beretrieved and analyzed to determine if the recommendation was followed.The weight given to a subsequent recommendation from a user may beincreased based on a high number of followed recommendations, a highpercentage of followed recommendations, or both. Similarly, a productcategory for the item recommendation may be stored in the recommendationrecord. The weight may be determined based on the number or percentageof followed recommendations within the product category for the currentrecommendation. For example, if the user has made many followedrecommendations for shoes, but the current recommended item is a car,the weight given to the user may be lower than if the currentrecommended item were also for a shoe.

In operation 1060, an item to recommend is determined (e.g., by thegeneration module 230), based on the recommendations and the weightedvalues of the recommending users. For example, each user'srecommendation may be given a multiplicative value equal to the numberof previously followed recommendations plus 1. Thus, where two usersrecommend a first brand and a third user recommends a second brand, butthe first two users have no previously followed recommendations whilethe third user has 3 previously followed recommendations, arecommendation for the second brand would be made. The first two userswould each have a weight of 1 (0+1). The third user would have a weightof 4 (3+1). The weight for the first brand would be 2, the sum of theweights of the first two users. The weight for the second brand would be4, the weight of the third user. Since the total weight for the secondbrand is higher than the total weight for the first brand, the item torecommend is determined to be the second brand.

According to various example embodiments, one or more of themethodologies described herein may facilitate social media-basedrecommendations. According to various example embodiments, one or moreof the methodologies described herein may facilitate providingrecommendations without accessing private data. For example, anadvertisement for a product mentioned in a private email and placed nextto the private email may show that the email provider is reading theuser's email. By contrast, an advertisement or recommendation for anitem recommended in a public or semi-public conversation thread may notimplicate the same invasion of privacy. Additionally, one or more themethodologies described herein may facilitate retrieval and presentationof results of interest to a user without requiring the user toexplicitly craft a query.

When these effects are considered in aggregate, one or more of themethodologies described herein may obviate a need for certain efforts orresources that otherwise would be involved in identifying items ofinterest. Efforts expended by a user in identifying relevant items maybe reduced by one or more of the methodologies described herein.Computing resources used by one or more machines, databases, or devices(e.g., within the network environment 100) may similarly be reduced.Examples of such computing resources include processor cycles, networktraffic, memory usage, data storage capacity, power consumption, andcooling capacity.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is tangibleunit capable of performing certain operations and may be configured orarranged in a certain manner. In example embodiments, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more processors may be configured by software (e.g., anapplication or application portion) as a hardware-implemented modulethat operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses) thatconnect the hardware-implemented modules. In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., Application Program Interfaces (APIs).

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures meritconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 11 is a block diagram of machine in the example form of a computersystem 1100 within which instructions 1124 may be executed for causingthe machine to perform any one or more of the methodologies discussedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, atablet, a wearable devic3e (e.g., a smart watch or smart glasses), a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 1100 includes a processor 1102 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1104 and a static memory 1106, which communicatewith each other via a bus 1108. The computer system 1100 may furtherinclude a video display unit 1110 (e.g., a liquid crystal display (LCD)or a cathode ray tube (CRT)). The computer system 1100 also includes analphanumeric input device 1112 (e.g., a keyboard or a touch-sensitivedisplay screen), a user interface (UI) navigation (or cursor control)device 1114 (e.g., a mouse), a disk drive unit 1116, a signal generationdevice 1118 (e.g., a speaker) and a network interface device 1120.

Machine-Readable Medium

The disk drive unit 1116 includes a computer-readable medium 1122 onwhich is stored one or more sets of data structures and instructions(e.g., software) 1124 embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 1124 mayalso reside, completely or at least partially, within the main memory1104 and/or within the processor 1102 during execution thereof by thecomputer system 1100, the main memory 1104 and the processor 1102 alsoconstituting machine-readable media.

While the machine-readable medium 1122 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 1124 or data structures. The term “machine-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions for execution bythe machine and that cause the machine to perform any one or more of themethodologies of the present inventive subject matter, or that iscapable of storing, encoding or carrying data structures utilized by orassociated with such instructions. The term “machine-readable medium”shall accordingly be taken to include, but not be limited to,solid-state memories, and optical and magnetic media. Specific examplesof machine-readable media include non-volatile memory, including by wayof example semiconductor memory devices, e.g., Erasable ProgrammableRead-Only Memory (EPROM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM), and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 1124 may further be transmitted or received over acommunications network 1126 using a transmission medium. Theinstructions 1124 may be transmitted using the network interface device1120 and any one of a number of well-known transfer protocols (e.g.,HTTP). Examples of communication networks include a local area network(“LAN”), a wide area network (“WAN”), the Internet, mobile telephonenetworks, Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., WiFi and WiMax networks). The term “transmission medium”shall be taken to include any intangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machine,and includes digital or analog communications signals or otherintangible media to facilitate communication of such software.

Although the inventive subject matter has been described with referenceto specific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof, show by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDetailed Description, therefore, is not to be taken in a limiting sense,and the scope of various embodiments is defined only by the appendedclaims, along with the full range of equivalents to which such claimsare entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A method comprising: receiving, from a useraccount, a request for a recommendation of an item provided by one ormore other user accounts; accessing one or more attributes associatedwith the user account; accessing comments provided by the one or moreother user accounts, the comments including one or more of opinions,commentary, or postings related to the item; for each of the one or moreuser accounts, determining a number of previously-followed comments thatare different from the accessed comments and were followed; parsing theaccessed comments to identify a set of items referred to in the accessedcomments; selecting a recommended item from the identified set of items,the recommended item being selected based on: a weight associated witheach comment of the accessed comments provided by the one or more otherusers; interactions for a user account of the one or more other useraccounts providing a comment; and the one or more attributes; anddetermining that a size of the set of comments exceeds a threshold and amomentum of a thread of the set of comments where at least one or moreprocessor cycles are reduced; and generating a user interface thatpresents information about the recommended item to the user based on thedetermination.
 2. The method of claim 1, wherein the interactionsinclude one or more of viewing various items, bidding on the variousitems, buying ones of the various items, sharing the various items onsocial networks.
 3. The method of claim 1, wherein the information aboutthe recommended item is presented alongside the accessed comments. 4.The method of claim 1, wherein the information about the recommendeditem is presented in-line with the accessed comments.
 5. The method ofclaim 1, wherein the method further comprises identifying a location ofa user associated with the user account and the selecting of therecommended item is further based on the location of the user.
 6. Themethod of claim 1, wherein the accessing comments provided by the one ormore other user accounts includes: accessing a set of comments by theone or more other user accounts; and for each comment in the set ofcomments: determining if a product is mentioned in the comment; and if aproduct is mentioned, determining if the comment is positive ornegative.
 7. The method of claim 1, wherein the method furthercomprises: accessing comments includes detecting, for each comment inthe accessed comments, a degree to which the comment was agreed with byusers; and selecting the recommended item from the identified set ofitems is based on the degree to which one or more comments correspondingto the recommended item were agreed with.
 8. The method of claim 1,wherein the accessed comments are a batch of comments, and the methodfurther comprises detecting an end of the batch of comments, thedetection being based on an elapsed time after a last comment exceedinga threshold without a further comment where the selecting of therecommended item occurs after the detecting of the end of the batch ofcomments.
 9. The method of claim 8, wherein the method furthercomprises: transmitting the user interface that presents informationabout the recommended item to the user; detecting a beginning of asecond batch of comments, the detection based on a first comment afterthe transmitting of the information about the recommended item;detecting an end of the second batch of comments, the detection based onan elapsed time after a second comment exceeding a threshold without anadditional comment; based on the second batch of comments, selecting asecond recommended item; and generating and transmitting a second userinterface that presents information about the second recommended item tothe user.
 10. The method of claim 1, wherein the one or more userattributes correspond to interests of the user.
 11. A system comprising:a processor; and a computer readable storage medium storing instructionsthat, when executed by the processor, cause the system to performoperations comprising: receiving, from a user account, a request for arecommendation of an item provided by one or more other user accounts;accessing one or more attributes associated with the user account;accessing comments provided by the one or more other user accounts, thecomments including one or more of opinions, commentary, or postingsrelated to the item; for each of the one or more user accounts,determining a number of previously-followed comments that are differentfrom the accessed comments and were followed; parsing the accessedcomments to identify a set of items referred to in the accessedcomments; selecting a recommended item from the identified set of items,the recommended item being selected based on: a weight associated witheach comment of the accessed comments provided by the one or more otherusers; interactions for a user account of the one or more other useraccounts providing a comment; and the one or more attributes; anddetermining that a size of the set of comments exceeds a threshold and amomentum of a thread of the set of comments where at least one or moreprocessor cycles are reduced; and generating a user interface thatpresents information about the recommended item to the user based on thedetermination.
 12. The system of claim 11, wherein the interactionsinclude one or more of viewing various items, bidding on the variousitems, buying ones of the various items, sharing the various items onsocial networks.
 13. The system of claim 11, wherein the informationabout the recommended item is presented either alongside the accessedcomments or in-line with the accessed comments.
 14. The system of claim11, wherein the operations further comprise identifying a location of auser associated with the user account and the selecting of therecommended item is further based on the location of the user.
 15. Thesystem of claim 11, wherein the accessing comments provided by the oneor more other user accounts includes: accessing a set of comments by theone or more other user accounts; and for each comment in the set ofcomments: determining if a product is mentioned in the comment; and if aproduct is mentioned, determining if the comment is positive ornegative.
 16. The system of claim 11, wherein the operations furthercomprise: accessing comments includes detecting, for each comment in theaccessed comments, a degree to which the comment was agreed with byusers; and selecting the recommended item from the identified set ofitems is based on the degree to which one or more comments correspondingto the recommended item were agreed with.
 17. The system of claim 11,wherein the accessed comments are a batch of comments, and theoperations further comprise: detecting an end of the batch of comments,the detection being based on an elapsed time after a last commentexceeding a threshold without a further comment where the selecting ofthe recommended item occurs after the detecting of the end of the batchof comments; transmitting the user interface that presents informationabout the recommended item to the user; detecting a beginning of asecond batch of comments, the detection based on a first comment afterthe transmitting of the information about the recommended item;detecting an end of the second batch of comments, the detection based onan elapsed time after a second comment exceeding a threshold without anadditional comment; based on the second batch of comments, selecting asecond recommended item; and generating and transmitting a second userinterface that presents information about the second recommended item tothe user.
 18. A non-transitory machine-readable storage mediumcomprising instructions that, when executed by one or more processors ofan application server comprising a communication module, a recognitionmodule, and a generation module, cause the application server to performoperations comprising: receiving, from a user account, a request for arecommendation of an item provided by one or more other user accounts;accessing one or more attributes associated with the user account;accessing comments provided by the one or more other user accounts, thecomments including one or more of opinions, commentary, or postingsrelated to the item; for each of the one or more user accounts,determining a number of previously-followed comments that are differentfrom the accessed comments and were followed; parsing the accessedcomments to identify a set of items referred to in the accessedcomments; selecting a recommended item from the identified set of items,the recommended item being selected based on: a weight associated witheach comment of the accessed comments provided by the one or more otherusers; interactions for a user account of the one or more other useraccounts providing a comment; and the one or more attributes; anddetermining that a size of the set of comments exceeds a threshold and amomentum of a thread of the set of comments where at least one or moreprocessor cycles are reduced; and generating a user interface thatpresents information about the recommended item to the user based on thedetermination.
 19. The non-transitory machine-readable storage medium ofclaim 18, wherein the operations further comprise: accessing commentsincludes detecting, for each comment in the accessed comments, a degreeto which the comment was agreed with by users; and selecting therecommended item from the identified set of items is based on the degreeto which one or more comments corresponding to the recommended item wereagreed with.
 20. The non-transitory machine-readable storage medium ofclaim 18, wherein the accessed comments are a batch of comments, and theoperations further comprise detecting an end of the batch of comments,the detection being based on an elapsed time after a last commentexceeding a threshold without a further comment where the selecting ofthe recommended item occurs after the detecting of the end of the batchof comments and the operations further comprise: transmitting the userinterface that presents information about the recommended item to theuser; detecting a beginning of a second batch of comments, the detectionbased on a first comment after the transmitting of the information aboutthe recommended item; detecting an end of the second batch of comments,the detection based on an elapsed time after a second comment exceedinga threshold without an additional comment; based on the second batch ofcomments, selecting a second recommended item; and generating andtransmitting a second user interface that presents information about thesecond recommended item to the user.